




'''simple docstring'''







from argparse import ArgumentParser

from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand


lowerCAmelCase_     						=				logging.get_logger(__name__)  # pylint: disable=invalid-name







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   if not path:
      return "pipe"

   for ext in PipelineDataFormat.SUPPORTED_FORMATS:
      if path.endswith(UpperCAmelCase						):
         return ext

   raise Exception(
       F"""Unable to determine file format from file extension {path}. """
       F"""Please provide the format through --format {PipelineDataFormat.SUPPORTED_FORMATS}"""						)







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   A__       =     pipeline(
       task=args.task      ,model=args.model if args.model else None      ,config=args.config      ,tokenizer=args.tokenizer      ,device=args.device      ,)
   A__       =     try_infer_format_from_ext(args.input						) if args.format == 'infer' else args.format
   A__       =     PipelineDataFormat.from_str(
       format=UpperCAmelCase      ,output_path=args.output      ,input_path=args.input      ,column=args.column if args.column else nlp.default_input_names      ,overwrite=args.overwrite      ,)
   return RunCommand(UpperCAmelCase      ,UpperCAmelCase						)







class 	_snake_case(			UpperCAmelCase      ):






 def __init__(self       :  Tuple    ,		a       :  Pipeline    ,		a       :  PipelineDataFormat    )       ->   Union[str, Any]:






    """simple docstring"""


    A__       =     nlp
    A__       =     reader






 @staticmethod
 def 						_UpperCamelCase (a       :  ArgumentParser    )       ->   str:






    """simple docstring"""


    A__       =     parser.add_parser('run'    ,		help='Run a pipeline through the CLI'    )
    run_parser.add_argument('--task'    ,		choices=get_supported_tasks()    ,		help='Task to run'    )
    run_parser.add_argument('--input'    ,		type=a    ,		help='Path to the file to use for inference'    )
    run_parser.add_argument('--output'    ,		type=a    ,		help='Path to the file that will be used post to write results.'    )
    run_parser.add_argument('--model'    ,		type=a    ,		help='Name or path to the model to instantiate.'    )
    run_parser.add_argument('--config'    ,		type=a    ,		help='Name or path to the model\'s config to instantiate.'    )
    run_parser.add_argument(
        '--tokenizer'    ,		type=a    ,		help='Name of the tokenizer to use. (default: same as the model name)'    )
    run_parser.add_argument(
        '--column'    ,		type=a    ,		help='Name of the column to use as input. (For multi columns input as QA use column1,columns2)'    ,		)
    run_parser.add_argument(
        '--format'    ,		type=a    ,		default='infer'    ,		choices=PipelineDataFormat.SUPPORTED_FORMATS    ,		help='Input format to read from'    ,		)
    run_parser.add_argument(
        '--device'    ,		type=a    ,		default=-1    ,		help='Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)'    ,		)
    run_parser.add_argument('--overwrite'    ,		action='store_true'    ,		help='Allow overwriting the output file.'    )
    run_parser.set_defaults(func=a    )






 def 						_UpperCamelCase (self       :  int    )       ->   Optional[Any]:






    """simple docstring"""


    A__  ,			A__       =     self._nlp, []

    for entry in self._reader:
       A__       =     nlp(**a    ) if self._reader.is_multi_columns else nlp(a    )
       if isinstance(a    ,		a    ):
          outputs.append(a    )
       else:
          outputs += output

        # Saving data
    if self._nlp.binary_output:
       A__       =     self._reader.save_binary(a    )
       logger.warning(f"""Current pipeline requires output to be in binary format, saving at {binary_path}"""    )
    else:
       self._reader.save(a    )








'''simple docstring'''







import os

import torch

from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version


if is_torch_version('''>=''', FSDP_PYTORCH_VERSION):
    import torch.distributed.checkpoint as dist_cp
    from torch.distributed.checkpoint.default_planner import DefaultLoadPlanner, DefaultSavePlanner
    from torch.distributed.checkpoint.optimizer import load_sharded_optimizer_state_dict
    from torch.distributed.fsdp.fully_sharded_data_parallel import FullyShardedDataParallel as FSDP
    from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType


lowerCAmelCase_     						=				get_logger(__name__)







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase=0						):






   '''simple docstring'''







   os.makedirs(UpperCAmelCase      ,exist_ok=UpperCAmelCase						)
   with FSDP.state_dict_type(
       UpperCAmelCase      ,fsdp_plugin.state_dict_type      ,fsdp_plugin.state_dict_config      ,fsdp_plugin.optim_state_dict_config						):
      A__       =     model.state_dict()
      if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
         A__       =     F"""{MODEL_NAME}.bin""" if model_index == 0 else F"""{MODEL_NAME}_{model_index}.bin"""
         A__       =     os.path.join(UpperCAmelCase      ,UpperCAmelCase						)
         if accelerator.process_index == 0:
            logger.info(F"""Saving model to {output_model_file}"""						)
            torch.save(UpperCAmelCase      ,UpperCAmelCase						)
            logger.info(F"""Model saved to {output_model_file}"""						)
      elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
         A__       =     (
             F"""{MODEL_NAME}_rank{accelerator.process_index}.bin"""
             if model_index == 0
             else F"""{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin"""
         )
         A__       =     os.path.join(UpperCAmelCase      ,UpperCAmelCase						)
         logger.info(F"""Saving model to {output_model_file}"""						)
         torch.save(UpperCAmelCase      ,UpperCAmelCase						)
         logger.info(F"""Model saved to {output_model_file}"""						)
      elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
         A__       =     os.path.join(UpperCAmelCase      ,F"""{MODEL_NAME}_{model_index}"""						)
         os.makedirs(UpperCAmelCase      ,exist_ok=UpperCAmelCase						)
         logger.info(F"""Saving model to {ckpt_dir}"""						)
         A__       =     {'model': state_dict}

         dist_cp.save_state_dict(
             state_dict=UpperCAmelCase      ,storage_writer=dist_cp.FileSystemWriter(UpperCAmelCase						)      ,planner=DefaultSavePlanner()      ,)
         logger.info(F"""Model saved to {ckpt_dir}"""						)







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase=0						):






   '''simple docstring'''







   accelerator.wait_for_everyone()
   with FSDP.state_dict_type(
       UpperCAmelCase      ,fsdp_plugin.state_dict_type      ,fsdp_plugin.state_dict_config      ,fsdp_plugin.optim_state_dict_config						):
      if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
         if type(UpperCAmelCase						) != FSDP and accelerator.process_index != 0:
            if not fsdp_plugin.sync_module_states:
               raise ValueError(
                   'Set the `sync_module_states` flag to `True` so that model states are synced across processes when '
                   'initializing FSDP object'						)
            return
         A__       =     F"""{MODEL_NAME}.bin""" if model_index == 0 else F"""{MODEL_NAME}_{model_index}.bin"""
         A__       =     os.path.join(UpperCAmelCase      ,UpperCAmelCase						)
         logger.info(F"""Loading model from {input_model_file}"""						)
         A__       =     torch.load(UpperCAmelCase						)
         logger.info(F"""Model loaded from {input_model_file}"""						)
      elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
         A__       =     (
             F"""{MODEL_NAME}_rank{accelerator.process_index}.bin"""
             if model_index == 0
             else F"""{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin"""
         )
         A__       =     os.path.join(UpperCAmelCase      ,UpperCAmelCase						)
         logger.info(F"""Loading model from {input_model_file}"""						)
         A__       =     torch.load(UpperCAmelCase						)
         logger.info(F"""Model loaded from {input_model_file}"""						)
      elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
         A__       =     (
             os.path.join(UpperCAmelCase      ,F"""{MODEL_NAME}_{model_index}"""						)
             if F"""{MODEL_NAME}""" not in input_dir
             else input_dir
         )
         logger.info(F"""Loading model from {ckpt_dir}"""						)
         A__       =     {'model': model.state_dict()}
         dist_cp.load_state_dict(
             state_dict=UpperCAmelCase      ,storage_reader=dist_cp.FileSystemReader(UpperCAmelCase						)      ,planner=DefaultLoadPlanner()      ,)
         A__       =     state_dict['model']
         logger.info(F"""Model loaded from {ckpt_dir}"""						)
      model.load_state_dict(UpperCAmelCase						)







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase=0						):






   '''simple docstring'''







   os.makedirs(UpperCAmelCase      ,exist_ok=UpperCAmelCase						)
   with FSDP.state_dict_type(
       UpperCAmelCase      ,fsdp_plugin.state_dict_type      ,fsdp_plugin.state_dict_config      ,fsdp_plugin.optim_state_dict_config						):
      A__       =     FSDP.optim_state_dict(UpperCAmelCase      ,UpperCAmelCase						)
      if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
         if accelerator.process_index == 0:
            A__       =     (
                F"""{OPTIMIZER_NAME}.bin""" if optimizer_index == 0 else F"""{OPTIMIZER_NAME}_{optimizer_index}.bin"""
            )
            A__       =     os.path.join(UpperCAmelCase      ,UpperCAmelCase						)
            logger.info(F"""Saving Optimizer state to {output_optimizer_file}"""						)
            torch.save(UpperCAmelCase      ,UpperCAmelCase						)
            logger.info(F"""Optimizer state saved in {output_optimizer_file}"""						)
      else:
         A__       =     os.path.join(UpperCAmelCase      ,F"""{OPTIMIZER_NAME}_{optimizer_index}"""						)
         os.makedirs(UpperCAmelCase      ,exist_ok=UpperCAmelCase						)
         logger.info(F"""Saving Optimizer state to {ckpt_dir}"""						)
         dist_cp.save_state_dict(
             state_dict={'optimizer': optim_state}      ,storage_writer=dist_cp.FileSystemWriter(UpperCAmelCase						)      ,planner=DefaultSavePlanner()      ,)
         logger.info(F"""Optimizer state saved in {ckpt_dir}"""						)







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase=0						):






   '''simple docstring'''







   accelerator.wait_for_everyone()
   with FSDP.state_dict_type(
       UpperCAmelCase      ,fsdp_plugin.state_dict_type      ,fsdp_plugin.state_dict_config      ,fsdp_plugin.optim_state_dict_config						):
      if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
         A__       =     None
         # below check should work but currently it isn't working (mostly opytorch issue),
         # in the meantime disabling it at the cost of excess memory usage
         # if accelerator.process_index == 0 or not fsdp_plugin.optim_state_dict_config.rank0_only:
         A__       =     (
             F"""{OPTIMIZER_NAME}.bin""" if optimizer_index == 0 else F"""{OPTIMIZER_NAME}_{optimizer_index}.bin"""
         )
         A__       =     os.path.join(UpperCAmelCase      ,UpperCAmelCase						)
         logger.info(F"""Loading Optimizer state from {input_optimizer_file}"""						)
         A__       =     torch.load(UpperCAmelCase						)
         logger.info(F"""Optimizer state loaded from {input_optimizer_file}"""						)
      else:
         A__       =     (
             os.path.join(UpperCAmelCase      ,F"""{OPTIMIZER_NAME}_{optimizer_index}"""						)
             if F"""{OPTIMIZER_NAME}""" not in input_dir
             else input_dir
         )
         logger.info(F"""Loading Optimizer from {ckpt_dir}"""						)
         A__       =     load_sharded_optimizer_state_dict(
             model_state_dict=model.state_dict()      ,optimizer_key='optimizer'      ,storage_reader=dist_cp.FileSystemReader(UpperCAmelCase						)      ,)
         A__       =     optim_state['optimizer']
         logger.info(F"""Optimizer loaded from {ckpt_dir}"""						)
      A__       =     FSDP.optim_state_dict_to_load(UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase						)
      optimizer.load_state_dict(UpperCAmelCase						)


